The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
Open accessAbstract
In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores. Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM (Recency - Frequency - Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object. Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture. The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 0.98.
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Self-citationData mining for credit card fraud: a comparative study
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“While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records.”
BackgroundDistributed data mining in credit card fraud detection
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Self-citationCredit Card Fraud Detection Using Hidden Markov Model
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Learned lessons in credit card fraud detection from a practitioner perspective
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Self-citationCredit card fraud detection with a neural-network
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Combining unsupervised and supervised learning in credit card fraud detection
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Self-citationFeature engineering strategies for credit card fraud detection
Correa Bahnsen, A., Aouada, D., Stojanovic, A., & Ottersten, B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.12.030 · Cited by 451
Zhang, X., Han, Y., Xu, W., & Wang, Q. (2021) · Information Sciences · doi:10.1016/j.ins.2019.05.023 · Cited by 283
Machine learning algorithms for social media analysis: A survey
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100395 · Cited by 276
SCARFF : A scalable framework for streaming credit card fraud detection with spark
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.09.005 · Cited by 267
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Metadata
- Authors
- Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B.
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 75
- Pages
- 38-48
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W614715210
- Cited by
- 368
- References
- 39
- FWCI
- 25.03
- Type
- journal article
- Language
- en
- Open access
- bronze
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